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Nonparametric estimation of average treatment effects under exogeneity: a review
 REVIEW OF ECONOMICS AND STATISTICS
, 2004
"... Recently there has been a surge in econometric work focusing on estimating average treatment effects under various sets of assumptions. One strand of this literature has developed methods for estimating average treatment effects for a binary treatment under assumptions variously described as exogen ..."
Abstract

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Recently there has been a surge in econometric work focusing on estimating average treatment effects under various sets of assumptions. One strand of this literature has developed methods for estimating average treatment effects for a binary treatment under assumptions variously described as exogeneity, unconfoundedness, or selection on observables. The implication of these assumptions is that systematic (for example, average or distributional) differences in outcomes between treated and control units with the same values for the covariates are attributable to the treatment. Recent analysis has considered estimation and inference for average treatment effects under weaker assumptions than typical of the earlier literature by avoiding distributional and functionalform assumptions. Various methods of semiparametric estimation have been proposed, including estimating the unknown regression functions, matching, methods using the propensity score such as weighting and blocking, and combinations of these approaches. In this paper I review the state of this
CONFIDENCE INTERVAL ESTIMATION IN HEAVYTAILED QUEUES USING CONTROL VARIATES AND BOOTSTRAP
"... The heavytailed condition of a random variable can cause difficulties in the estimation of parameters and their confidence intervals from simulations, specially if the variance of the random variable we are studying is infinite. If we use a standard method to obtain confidence intervals under such ..."
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The heavytailed condition of a random variable can cause difficulties in the estimation of parameters and their confidence intervals from simulations, specially if the variance of the random variable we are studying is infinite. If we use a standard method to obtain confidence intervals under such circumstances we shall typically get inaccurate results. To face up this problem, and trying to contribute to find accurate confidence interval estimation methods for such cases, in this paper we propose the use of a control variate method combined with a bootstrap based confidence interval computation. The control variate approach is doubly interesting to address the problem of infinite variance. We tested this approach in a M/P/1 queue system with infinite variance in the queue waiting time and got quite accurate results. 1